Abstract: The paper investigates the potential benefits of bringing together Internet of Things and deep learning techniques toward the development of assistive technologies for users with visual disabilities. We propose a computer vision system designed to classify objects in the user's surroundings and to provide its user with an audio description of the detected things. The solution exploits a wearable vision sensor, which is mounted on the user's glasses and controlled by a single board computer running Google TensorFlow framework. With this software layer, the aid may support users in specific environments, e.g., museums. Finally, experiments show promising results in the context of image classification.

TensorFlow is an open source deep learning framework developed at Google that enables developers to conceive a wide variety of applications based on artificial intelligence principles. In this paper, we employ such software resources towards the development of a computer vision system for people with visual impairments. We propose a wearable assistive technology solution consisting of a single board computer connected to a camera mounted on the user's glasses. A TensorFlow based software runs on the board in order to real time classify the images captured by the camera, while a text to speech process vocalizes the still's content for the blind person. In this way, the system provides an audio description of the objects in the user's surrounding environment and it may help these people to better detect the things around them.

In the field of deep learning, this paper presents the design of a wearable computer vision system for visually impaired users. The Assistive Technology solution exploits a powerful single board computer and smart glasses with a camera in order to allow its user to explore the objects within his surrounding environment, while it employs Google TensorFlow machine learning framework in order to real time classify the acquired stills. Therefore the proposed aid can increase the awareness of the explored environment and it interacts with its user by means of audio messages.